Overview

Dataset statistics

Number of variables22
Number of observations583985
Missing cells654318
Missing cells (%)5.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory98.0 MiB
Average record size in memory176.0 B

Variable types

Numeric11
Categorical10
Unsupported1

Alerts

TAIL_NUM has a high cardinality: 5446 distinct valuesHigh cardinality
ORIGIN has a high cardinality: 346 distinct valuesHigh cardinality
DEST has a high cardinality: 346 distinct valuesHigh cardinality
OP_CARRIER_AIRLINE_ID is highly overall correlated with OP_UNIQUE_CARRIER and 1 other fieldsHigh correlation
ORIGIN_AIRPORT_ID is highly overall correlated with ORIGIN_AIRPORT_SEQ_IDHigh correlation
ORIGIN_AIRPORT_SEQ_ID is highly overall correlated with ORIGIN_AIRPORT_IDHigh correlation
DEST_AIRPORT_ID is highly overall correlated with DEST_AIRPORT_SEQ_IDHigh correlation
DEST_AIRPORT_SEQ_ID is highly overall correlated with DEST_AIRPORT_IDHigh correlation
DEP_TIME is highly overall correlated with ARR_TIME and 1 other fieldsHigh correlation
ARR_TIME is highly overall correlated with DEP_TIME and 2 other fieldsHigh correlation
OP_UNIQUE_CARRIER is highly overall correlated with OP_CARRIER_AIRLINE_ID and 1 other fieldsHigh correlation
OP_CARRIER is highly overall correlated with OP_CARRIER_AIRLINE_ID and 1 other fieldsHigh correlation
DEP_DEL15 is highly overall correlated with ARR_DEL15High correlation
DEP_TIME_BLK is highly overall correlated with DEP_TIME and 1 other fieldsHigh correlation
ARR_DEL15 is highly overall correlated with DEP_DEL15 and 2 other fieldsHigh correlation
CANCELLED is highly overall correlated with ARR_TIME and 1 other fieldsHigh correlation
DIVERTED is highly overall correlated with ARR_DEL15High correlation
CANCELLED is highly imbalanced (81.2%)Imbalance
DIVERTED is highly imbalanced (97.7%)Imbalance
DEP_TIME has 16352 (2.8%) missing valuesMissing
DEP_DEL15 has 16355 (2.8%) missing valuesMissing
ARR_TIME has 17061 (2.9%) missing valuesMissing
ARR_DEL15 has 18022 (3.1%) missing valuesMissing
Unnamed: 21 has 583985 (100.0%) missing valuesMissing
Unnamed: 21 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-03-27 15:39:18.665859
Analysis finished2023-03-27 15:40:08.251346
Duration49.59 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

DAY_OF_MONTH
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.960088
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-03-27T21:10:08.319362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9879417
Coefficient of variation (CV)0.56315114
Kurtosis-1.2118653
Mean15.960088
Median Absolute Deviation (MAD)8
Skewness0.013790461
Sum9320452
Variance80.783096
MonotonicityIncreasing
2023-03-27T21:10:08.399380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 20384
 
3.5%
11 20082
 
3.4%
25 20041
 
3.4%
7 20015
 
3.4%
18 20009
 
3.4%
10 19980
 
3.4%
24 19963
 
3.4%
31 19962
 
3.4%
17 19960
 
3.4%
14 19941
 
3.4%
Other values (21) 383648
65.7%
ValueCountFrequency (%)
1 18009
3.1%
2 20384
3.5%
3 19522
3.3%
4 19566
3.4%
5 16807
2.9%
6 19448
3.3%
7 20015
3.4%
8 18815
3.2%
9 19236
3.3%
10 19980
3.4%
ValueCountFrequency (%)
31 19962
3.4%
30 19102
3.3%
29 18662
3.2%
28 19934
3.4%
27 18575
3.2%
26 15267
2.6%
25 20041
3.4%
24 19963
3.4%
23 19099
3.3%
22 18657
3.2%

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8356259
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-03-27T21:10:08.475397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9218991
Coefficient of variation (CV)0.50106531
Kurtosis-1.1030354
Mean3.8356259
Median Absolute Deviation (MAD)2
Skewness0.15555045
Sum2239948
Variance3.6936961
MonotonicityNot monotonic
2023-03-27T21:10:08.538411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 99387
17.0%
3 96920
16.6%
2 92796
15.9%
5 79698
13.6%
1 79401
13.6%
7 73459
12.6%
6 62324
10.7%
ValueCountFrequency (%)
1 79401
13.6%
2 92796
15.9%
3 96920
16.6%
4 99387
17.0%
5 79698
13.6%
6 62324
10.7%
7 73459
12.6%
ValueCountFrequency (%)
7 73459
12.6%
6 62324
10.7%
5 79698
13.6%
4 99387
17.0%
3 96920
16.6%
2 92796
15.9%
1 79401
13.6%
Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
WN
111312 
AA
77017 
DL
73836 
OO
64926 
UA
46915 
Other values (12)
209979 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1167970
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 111312
19.1%
AA 77017
13.2%
DL 73836
12.6%
OO 64926
11.1%
UA 46915
8.0%
YX 25755
 
4.4%
MQ 25699
 
4.4%
B6 24443
 
4.2%
OH 23169
 
4.0%
AS 20744
 
3.6%
Other values (7) 90169
15.4%

Length

2023-03-27T21:10:08.615428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 111312
19.1%
aa 77017
13.2%
dl 73836
12.6%
oo 64926
11.1%
ua 46915
8.0%
yx 25755
 
4.4%
mq 25699
 
4.4%
b6 24443
 
4.2%
oh 23169
 
4.0%
as 20744
 
3.6%
Other values (7) 90169
15.4%

Most occurring characters

ValueCountFrequency (%)
A 228491
19.6%
O 153021
13.1%
N 126534
10.8%
W 111312
9.5%
D 73836
 
6.3%
L 73836
 
6.3%
U 46915
 
4.0%
Y 44101
 
3.8%
E 33393
 
2.9%
V 31541
 
2.7%
Other values (12) 244990
21.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1106919
94.8%
Decimal Number 61051
 
5.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 228491
20.6%
O 153021
13.8%
N 126534
11.4%
W 111312
10.1%
D 73836
 
6.7%
L 73836
 
6.7%
U 46915
 
4.2%
Y 44101
 
4.0%
E 33393
 
3.0%
V 31541
 
2.8%
Other values (9) 183939
16.6%
Decimal Number
ValueCountFrequency (%)
9 29845
48.9%
6 24443
40.0%
4 6763
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1106919
94.8%
Common 61051
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 228491
20.6%
O 153021
13.8%
N 126534
11.4%
W 111312
10.1%
D 73836
 
6.7%
L 73836
 
6.7%
U 46915
 
4.2%
Y 44101
 
4.0%
E 33393
 
3.0%
V 31541
 
2.8%
Other values (9) 183939
16.6%
Common
ValueCountFrequency (%)
9 29845
48.9%
6 24443
40.0%
4 6763
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1167970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 228491
19.6%
O 153021
13.1%
N 126534
10.8%
W 111312
9.5%
D 73836
 
6.3%
L 73836
 
6.3%
U 46915
 
4.0%
Y 44101
 
3.8%
E 33393
 
2.9%
V 31541
 
2.7%
Other values (12) 244990
21.0%

OP_CARRIER_AIRLINE_ID
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19983.213
Minimum19393
Maximum20452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-03-27T21:10:08.689445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum19393
5-th percentile19393
Q119790
median19977
Q320368
95-th percentile20436
Maximum20452
Range1059
Interquartile range (IQR)578

Descriptive statistics

Standard deviation377.72464
Coefficient of variation (CV)0.018902097
Kurtosis-1.3049711
Mean19983.213
Median Absolute Deviation (MAD)386
Skewness-0.26626807
Sum1.1669897 × 1010
Variance142675.9
MonotonicityNot monotonic
2023-03-27T21:10:08.767463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
19393 111312
19.1%
19805 77017
13.2%
19790 73836
12.6%
20304 64926
11.1%
19977 46915
8.0%
20452 25755
 
4.4%
20398 25699
 
4.4%
20409 24443
 
4.2%
20397 23169
 
4.0%
19930 20744
 
3.6%
Other values (7) 90169
15.4%
ValueCountFrequency (%)
19393 111312
19.1%
19690 6798
 
1.2%
19790 73836
12.6%
19805 77017
13.2%
19930 20744
 
3.6%
19977 46915
8.0%
20304 64926
11.1%
20363 20198
 
3.5%
20366 13195
 
2.3%
20368 6763
 
1.2%
ValueCountFrequency (%)
20452 25755
4.4%
20436 9647
 
1.7%
20416 15222
2.6%
20409 24443
4.2%
20398 25699
4.4%
20397 23169
4.0%
20378 18346
3.1%
20368 6763
 
1.2%
20366 13195
2.3%
20363 20198
3.5%

OP_CARRIER
Categorical

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
WN
111312 
AA
77017 
DL
73836 
OO
64926 
UA
46915 
Other values (12)
209979 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1167970
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 111312
19.1%
AA 77017
13.2%
DL 73836
12.6%
OO 64926
11.1%
UA 46915
8.0%
YX 25755
 
4.4%
MQ 25699
 
4.4%
B6 24443
 
4.2%
OH 23169
 
4.0%
AS 20744
 
3.6%
Other values (7) 90169
15.4%

Length

2023-03-27T21:10:08.848481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 111312
19.1%
aa 77017
13.2%
dl 73836
12.6%
oo 64926
11.1%
ua 46915
8.0%
yx 25755
 
4.4%
mq 25699
 
4.4%
b6 24443
 
4.2%
oh 23169
 
4.0%
as 20744
 
3.6%
Other values (7) 90169
15.4%

Most occurring characters

ValueCountFrequency (%)
A 228491
19.6%
O 153021
13.1%
N 126534
10.8%
W 111312
9.5%
D 73836
 
6.3%
L 73836
 
6.3%
U 46915
 
4.0%
Y 44101
 
3.8%
E 33393
 
2.9%
V 31541
 
2.7%
Other values (12) 244990
21.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1106919
94.8%
Decimal Number 61051
 
5.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 228491
20.6%
O 153021
13.8%
N 126534
11.4%
W 111312
10.1%
D 73836
 
6.7%
L 73836
 
6.7%
U 46915
 
4.2%
Y 44101
 
4.0%
E 33393
 
3.0%
V 31541
 
2.8%
Other values (9) 183939
16.6%
Decimal Number
ValueCountFrequency (%)
9 29845
48.9%
6 24443
40.0%
4 6763
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1106919
94.8%
Common 61051
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 228491
20.6%
O 153021
13.8%
N 126534
11.4%
W 111312
10.1%
D 73836
 
6.7%
L 73836
 
6.7%
U 46915
 
4.2%
Y 44101
 
4.0%
E 33393
 
3.0%
V 31541
 
2.8%
Other values (9) 183939
16.6%
Common
ValueCountFrequency (%)
9 29845
48.9%
6 24443
40.0%
4 6763
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1167970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 228491
19.6%
O 153021
13.1%
N 126534
10.8%
W 111312
9.5%
D 73836
 
6.3%
L 73836
 
6.3%
U 46915
 
4.0%
Y 44101
 
3.8%
E 33393
 
2.9%
V 31541
 
2.7%
Other values (12) 244990
21.0%

TAIL_NUM
Categorical

Distinct5446
Distinct (%)0.9%
Missing2543
Missing (%)0.4%
Memory size4.5 MiB
N488HA
 
361
N481HA
 
348
N483HA
 
346
N489HA
 
344
N493HA
 
328
Other values (5441)
579715 

Length

Max length6
Median length6
Mean length5.986033
Min length3

Characters and Unicode

Total characters3480531
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)< 0.1%

Sample

1st rowN8688C
2nd rowN348PQ
3rd rowN8896A
4th rowN8886A
5th rowN8974C

Common Values

ValueCountFrequency (%)
N488HA 361
 
0.1%
N481HA 348
 
0.1%
N483HA 346
 
0.1%
N489HA 344
 
0.1%
N493HA 328
 
0.1%
N485HA 315
 
0.1%
N492HA 305
 
0.1%
N486HA 290
 
< 0.1%
N480HA 281
 
< 0.1%
N479HA 280
 
< 0.1%
Other values (5436) 578244
99.0%
(Missing) 2543
 
0.4%

Length

2023-03-27T21:10:08.926498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n488ha 361
 
0.1%
n481ha 348
 
0.1%
n483ha 346
 
0.1%
n489ha 344
 
0.1%
n493ha 328
 
0.1%
n485ha 315
 
0.1%
n492ha 305
 
0.1%
n486ha 290
 
< 0.1%
n480ha 281
 
< 0.1%
n479ha 280
 
< 0.1%
Other values (5436) 578244
99.4%

Most occurring characters

ValueCountFrequency (%)
N 757622
21.8%
8 229511
 
6.6%
9 214602
 
6.2%
7 203303
 
5.8%
2 199511
 
5.7%
3 195711
 
5.6%
5 188043
 
5.4%
1 182038
 
5.2%
6 181942
 
5.2%
4 175544
 
5.0%
Other values (24) 952704
27.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1901962
54.6%
Uppercase Letter 1578569
45.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 757622
48.0%
A 141091
 
8.9%
W 110015
 
7.0%
S 93262
 
5.9%
D 46495
 
2.9%
U 44283
 
2.8%
J 41335
 
2.6%
B 40318
 
2.6%
E 36500
 
2.3%
K 34466
 
2.2%
Other values (14) 233182
 
14.8%
Decimal Number
ValueCountFrequency (%)
8 229511
12.1%
9 214602
11.3%
7 203303
10.7%
2 199511
10.5%
3 195711
10.3%
5 188043
9.9%
1 182038
9.6%
6 181942
9.6%
4 175544
9.2%
0 131757
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1901962
54.6%
Latin 1578569
45.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 757622
48.0%
A 141091
 
8.9%
W 110015
 
7.0%
S 93262
 
5.9%
D 46495
 
2.9%
U 44283
 
2.8%
J 41335
 
2.6%
B 40318
 
2.6%
E 36500
 
2.3%
K 34466
 
2.2%
Other values (14) 233182
 
14.8%
Common
ValueCountFrequency (%)
8 229511
12.1%
9 214602
11.3%
7 203303
10.7%
2 199511
10.5%
3 195711
10.3%
5 188043
9.9%
1 182038
9.6%
6 181942
9.6%
4 175544
9.2%
0 131757
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3480531
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 757622
21.8%
8 229511
 
6.6%
9 214602
 
6.2%
7 203303
 
5.8%
2 199511
 
5.7%
3 195711
 
5.6%
5 188043
 
5.4%
1 182038
 
5.2%
6 181942
 
5.2%
4 175544
 
5.0%
Other values (24) 952704
27.4%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct6839
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2537.8693
Minimum1
Maximum7439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-03-27T21:10:09.019519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile232
Q1979
median2114
Q33902
95-th percentile5831
Maximum7439
Range7438
Interquartile range (IQR)2923

Descriptive statistics

Standard deviation1821.7361
Coefficient of variation (CV)0.7178211
Kurtosis-0.82533758
Mean2537.8693
Median Absolute Deviation (MAD)1386
Skewness0.54136521
Sum1.4820776 × 109
Variance3318722.6
MonotonicityNot monotonic
2023-03-27T21:10:09.115540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
403 310
 
0.1%
761 291
 
< 0.1%
465 287
 
< 0.1%
466 285
 
< 0.1%
546 283
 
< 0.1%
1106 282
 
< 0.1%
425 281
 
< 0.1%
794 278
 
< 0.1%
347 277
 
< 0.1%
55 276
 
< 0.1%
Other values (6829) 581135
99.5%
ValueCountFrequency (%)
1 136
< 0.1%
2 122
< 0.1%
3 159
< 0.1%
4 180
< 0.1%
5 105
< 0.1%
6 81
 
< 0.1%
7 169
< 0.1%
8 206
< 0.1%
9 162
< 0.1%
10 122
< 0.1%
ValueCountFrequency (%)
7439 54
< 0.1%
7438 54
< 0.1%
7437 27
< 0.1%
7436 31
< 0.1%
7435 7
 
< 0.1%
7434 41
< 0.1%
7433 31
< 0.1%
7432 62
< 0.1%
7431 27
< 0.1%
7430 31
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct346
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12659.702
Minimum10135
Maximum16218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-03-27T21:10:09.219563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q313931
95-th percentile14893
Maximum16218
Range6083
Interquartile range (IQR)2639

Descriptive statistics

Standard deviation1519.4055
Coefficient of variation (CV)0.12001906
Kurtosis-1.2976461
Mean12659.702
Median Absolute Deviation (MAD)1456
Skewness0.088227442
Sum7.3930761 × 109
Variance2308593.1
MonotonicityNot monotonic
2023-03-27T21:10:09.317586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 31155
 
5.3%
13930 26216
 
4.5%
11298 23063
 
3.9%
11057 19100
 
3.3%
11292 18507
 
3.2%
12892 17988
 
3.1%
14107 14761
 
2.5%
12266 14598
 
2.5%
12953 13872
 
2.4%
14771 13689
 
2.3%
Other values (336) 391036
67.0%
ValueCountFrequency (%)
10135 339
 
0.1%
10136 170
 
< 0.1%
10140 1731
0.3%
10141 62
 
< 0.1%
10146 84
 
< 0.1%
10155 116
 
< 0.1%
10157 129
 
< 0.1%
10158 298
 
0.1%
10165 9
 
< 0.1%
10170 53
 
< 0.1%
ValueCountFrequency (%)
16218 115
 
< 0.1%
15991 60
 
< 0.1%
15919 1048
0.2%
15841 62
 
< 0.1%
15624 494
 
0.1%
15607 87
 
< 0.1%
15582 53
 
< 0.1%
15454 58
 
< 0.1%
15412 1350
0.2%
15411 116
 
< 0.1%

ORIGIN_AIRPORT_SEQ_ID
Real number (ℝ)

Distinct346
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1265974.1
Minimum1013505
Maximum1621802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-03-27T21:10:09.748682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1013505
5-th percentile1039707
Q11129202
median1288903
Q31393102
95-th percentile1489302
Maximum1621802
Range608297
Interquartile range (IQR)263900

Descriptive statistics

Standard deviation151940.3
Coefficient of variation (CV)0.12001849
Kurtosis-1.2976523
Mean1265974.1
Median Absolute Deviation (MAD)145601
Skewness0.08822968
Sum7.393099 × 1011
Variance2.3085855 × 1010
MonotonicityNot monotonic
2023-03-27T21:10:09.851705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1039707 31155
 
5.3%
1393007 26216
 
4.5%
1129806 23063
 
3.9%
1105703 19100
 
3.3%
1129202 18507
 
3.2%
1289208 17988
 
3.1%
1410702 14761
 
2.5%
1226603 14598
 
2.5%
1295304 13872
 
2.4%
1477104 13689
 
2.3%
Other values (336) 391036
67.0%
ValueCountFrequency (%)
1013505 339
 
0.1%
1013603 170
 
< 0.1%
1014005 1731
0.3%
1014106 62
 
< 0.1%
1014602 84
 
< 0.1%
1015502 116
 
< 0.1%
1015706 129
 
< 0.1%
1015804 298
 
0.1%
1016506 9
 
< 0.1%
1017004 53
 
< 0.1%
ValueCountFrequency (%)
1621802 115
 
< 0.1%
1599102 60
 
< 0.1%
1591904 1048
0.2%
1584102 62
 
< 0.1%
1562404 494
 
0.1%
1560702 87
 
< 0.1%
1558203 53
 
< 0.1%
1545405 58
 
< 0.1%
1541205 1350
0.2%
1541106 116
 
< 0.1%

ORIGIN
Categorical

Distinct346
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
ATL
 
31155
ORD
 
26216
DFW
 
23063
CLT
 
19100
DEN
 
18507
Other values (341)
465944 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1751955
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGNV
2nd rowMSP
3rd rowDTW
4th rowTLH
5th rowATL

Common Values

ValueCountFrequency (%)
ATL 31155
 
5.3%
ORD 26216
 
4.5%
DFW 23063
 
3.9%
CLT 19100
 
3.3%
DEN 18507
 
3.2%
LAX 17988
 
3.1%
PHX 14761
 
2.5%
IAH 14598
 
2.5%
LGA 13872
 
2.4%
SFO 13689
 
2.3%
Other values (336) 391036
67.0%

Length

2023-03-27T21:10:09.944726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
atl 31155
 
5.3%
ord 26216
 
4.5%
dfw 23063
 
3.9%
clt 19100
 
3.3%
den 18507
 
3.2%
lax 17988
 
3.1%
phx 14761
 
2.5%
iah 14598
 
2.5%
lga 13872
 
2.4%
sfo 13689
 
2.3%
Other values (336) 391036
67.0%

Most occurring characters

ValueCountFrequency (%)
A 197390
 
11.3%
L 168284
 
9.6%
S 143334
 
8.2%
D 135780
 
7.8%
T 100119
 
5.7%
O 93364
 
5.3%
C 90737
 
5.2%
M 77481
 
4.4%
F 73904
 
4.2%
P 68946
 
3.9%
Other values (16) 602616
34.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1751955
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 197390
 
11.3%
L 168284
 
9.6%
S 143334
 
8.2%
D 135780
 
7.8%
T 100119
 
5.7%
O 93364
 
5.3%
C 90737
 
5.2%
M 77481
 
4.4%
F 73904
 
4.2%
P 68946
 
3.9%
Other values (16) 602616
34.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1751955
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 197390
 
11.3%
L 168284
 
9.6%
S 143334
 
8.2%
D 135780
 
7.8%
T 100119
 
5.7%
O 93364
 
5.3%
C 90737
 
5.2%
M 77481
 
4.4%
F 73904
 
4.2%
P 68946
 
3.9%
Other values (16) 602616
34.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1751955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 197390
 
11.3%
L 168284
 
9.6%
S 143334
 
8.2%
D 135780
 
7.8%
T 100119
 
5.7%
O 93364
 
5.3%
C 90737
 
5.2%
M 77481
 
4.4%
F 73904
 
4.2%
P 68946
 
3.9%
Other values (16) 602616
34.4%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct346
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12659.47
Minimum10135
Maximum16218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-03-27T21:10:10.032746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q313931
95-th percentile14893
Maximum16218
Range6083
Interquartile range (IQR)2639

Descriptive statistics

Standard deviation1519.3365
Coefficient of variation (CV)0.1200158
Kurtosis-1.2977051
Mean12659.47
Median Absolute Deviation (MAD)1456
Skewness0.088333171
Sum7.3929406 × 109
Variance2308383.3
MonotonicityNot monotonic
2023-03-27T21:10:10.129768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 31151
 
5.3%
13930 26212
 
4.5%
11298 23078
 
4.0%
11057 19105
 
3.3%
11292 18498
 
3.2%
12892 17977
 
3.1%
14107 14764
 
2.5%
12266 14586
 
2.5%
12953 13882
 
2.4%
14771 13702
 
2.3%
Other values (336) 391030
67.0%
ValueCountFrequency (%)
10135 340
 
0.1%
10136 170
 
< 0.1%
10140 1732
0.3%
10141 62
 
< 0.1%
10146 84
 
< 0.1%
10155 116
 
< 0.1%
10157 130
 
< 0.1%
10158 298
 
0.1%
10165 9
 
< 0.1%
10170 53
 
< 0.1%
ValueCountFrequency (%)
16218 115
 
< 0.1%
15991 60
 
< 0.1%
15919 1049
0.2%
15841 62
 
< 0.1%
15624 494
 
0.1%
15607 87
 
< 0.1%
15582 53
 
< 0.1%
15454 59
 
< 0.1%
15412 1350
0.2%
15411 116
 
< 0.1%

DEST_AIRPORT_SEQ_ID
Real number (ℝ)

Distinct346
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1265950.9
Minimum1013505
Maximum1621802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-03-27T21:10:10.234791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1013505
5-th percentile1039707
Q11129202
median1288903
Q31393102
95-th percentile1489302
Maximum1621802
Range608297
Interquartile range (IQR)263900

Descriptive statistics

Standard deviation151933.4
Coefficient of variation (CV)0.12001523
Kurtosis-1.2977113
Mean1265950.9
Median Absolute Deviation (MAD)145601
Skewness0.088335412
Sum7.3929635 × 1011
Variance2.3083757 × 1010
MonotonicityNot monotonic
2023-03-27T21:10:10.332813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1039707 31151
 
5.3%
1393007 26212
 
4.5%
1129806 23078
 
4.0%
1105703 19105
 
3.3%
1129202 18498
 
3.2%
1289208 17977
 
3.1%
1410702 14764
 
2.5%
1226603 14586
 
2.5%
1295304 13882
 
2.4%
1477104 13702
 
2.3%
Other values (336) 391030
67.0%
ValueCountFrequency (%)
1013505 340
 
0.1%
1013603 170
 
< 0.1%
1014005 1732
0.3%
1014106 62
 
< 0.1%
1014602 84
 
< 0.1%
1015502 116
 
< 0.1%
1015706 130
 
< 0.1%
1015804 298
 
0.1%
1016506 9
 
< 0.1%
1017004 53
 
< 0.1%
ValueCountFrequency (%)
1621802 115
 
< 0.1%
1599102 60
 
< 0.1%
1591904 1049
0.2%
1584102 62
 
< 0.1%
1562404 494
 
0.1%
1560702 87
 
< 0.1%
1558203 53
 
< 0.1%
1545405 59
 
< 0.1%
1541205 1350
0.2%
1541106 116
 
< 0.1%

DEST
Categorical

Distinct346
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
ATL
 
31151
ORD
 
26212
DFW
 
23078
CLT
 
19105
DEN
 
18498
Other values (341)
465941 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1751955
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowATL
2nd rowCVG
3rd rowCVG
4th rowATL
5th rowFSM

Common Values

ValueCountFrequency (%)
ATL 31151
 
5.3%
ORD 26212
 
4.5%
DFW 23078
 
4.0%
CLT 19105
 
3.3%
DEN 18498
 
3.2%
LAX 17977
 
3.1%
PHX 14764
 
2.5%
IAH 14586
 
2.5%
LGA 13882
 
2.4%
SFO 13702
 
2.3%
Other values (336) 391030
67.0%

Length

2023-03-27T21:10:10.426834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
atl 31151
 
5.3%
ord 26212
 
4.5%
dfw 23078
 
4.0%
clt 19105
 
3.3%
den 18498
 
3.2%
lax 17977
 
3.1%
phx 14764
 
2.5%
iah 14586
 
2.5%
lga 13882
 
2.4%
sfo 13702
 
2.3%
Other values (336) 391030
67.0%

Most occurring characters

ValueCountFrequency (%)
A 197382
 
11.3%
L 168337
 
9.6%
S 143375
 
8.2%
D 135775
 
7.7%
T 100066
 
5.7%
O 93371
 
5.3%
C 90766
 
5.2%
M 77477
 
4.4%
F 73910
 
4.2%
P 68937
 
3.9%
Other values (16) 602559
34.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1751955
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 197382
 
11.3%
L 168337
 
9.6%
S 143375
 
8.2%
D 135775
 
7.7%
T 100066
 
5.7%
O 93371
 
5.3%
C 90766
 
5.2%
M 77477
 
4.4%
F 73910
 
4.2%
P 68937
 
3.9%
Other values (16) 602559
34.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1751955
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 197382
 
11.3%
L 168337
 
9.6%
S 143375
 
8.2%
D 135775
 
7.7%
T 100066
 
5.7%
O 93371
 
5.3%
C 90766
 
5.2%
M 77477
 
4.4%
F 73910
 
4.2%
P 68937
 
3.9%
Other values (16) 602559
34.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1751955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 197382
 
11.3%
L 168337
 
9.6%
S 143375
 
8.2%
D 135775
 
7.7%
T 100066
 
5.7%
O 93371
 
5.3%
C 90766
 
5.2%
M 77477
 
4.4%
F 73910
 
4.2%
P 68937
 
3.9%
Other values (16) 602559
34.4%

DEP_TIME
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1438
Distinct (%)0.3%
Missing16352
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean1331.9578
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-03-27T21:10:10.516855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile603
Q1921
median1328
Q31738
95-th percentile2128
Maximum2400
Range2399
Interquartile range (IQR)817

Descriptive statistics

Standard deviation495.40402
Coefficient of variation (CV)0.37193672
Kurtosis-0.98525653
Mean1331.9578
Median Absolute Deviation (MAD)409
Skewness0.024633579
Sum7.5606321 × 108
Variance245425.14
MonotonicityNot monotonic
2023-03-27T21:10:10.616877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1382
 
0.2%
557 1237
 
0.2%
556 1205
 
0.2%
554 1191
 
0.2%
558 1136
 
0.2%
655 1123
 
0.2%
559 1074
 
0.2%
553 1067
 
0.2%
654 1014
 
0.2%
656 1010
 
0.2%
Other values (1428) 556194
95.2%
(Missing) 16352
 
2.8%
ValueCountFrequency (%)
1 45
< 0.1%
2 49
< 0.1%
3 47
< 0.1%
4 43
< 0.1%
5 33
< 0.1%
6 33
< 0.1%
7 31
< 0.1%
8 33
< 0.1%
9 26
< 0.1%
10 40
< 0.1%
ValueCountFrequency (%)
2400 46
< 0.1%
2359 52
< 0.1%
2358 64
< 0.1%
2357 57
< 0.1%
2356 70
< 0.1%
2355 79
< 0.1%
2354 75
< 0.1%
2353 93
< 0.1%
2352 72
< 0.1%
2351 83
< 0.1%

DEP_DEL15
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing16355
Missing (%)2.8%
Memory size4.5 MiB
0.0
468703 
1.0
98927 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1702890
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 468703
80.3%
1.0 98927
 
16.9%
(Missing) 16355
 
2.8%

Length

2023-03-27T21:10:10.700896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-27T21:10:10.795624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 468703
82.6%
1.0 98927
 
17.4%

Most occurring characters

ValueCountFrequency (%)
0 1036333
60.9%
. 567630
33.3%
1 98927
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1135260
66.7%
Other Punctuation 567630
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1036333
91.3%
1 98927
 
8.7%
Other Punctuation
ValueCountFrequency (%)
. 567630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1702890
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1036333
60.9%
. 567630
33.3%
1 98927
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1702890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1036333
60.9%
. 567630
33.3%
1 98927
 
5.8%

DEP_TIME_BLK
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0600-0659
41971 
0800-0859
 
39031
0700-0759
 
38450
1700-1759
 
37254
1200-1259
 
36918
Other values (14)
390361 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters5255865
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0600-0659
2nd row1400-1459
3rd row1200-1259
4th row1500-1559
5th row1900-1959

Common Values

ValueCountFrequency (%)
0600-0659 41971
 
7.2%
0800-0859 39031
 
6.7%
0700-0759 38450
 
6.6%
1700-1759 37254
 
6.4%
1200-1259 36918
 
6.3%
1100-1159 36774
 
6.3%
1500-1559 35988
 
6.2%
1400-1459 35925
 
6.2%
1000-1059 35768
 
6.1%
1600-1659 35250
 
6.0%
Other values (9) 210656
36.1%

Length

2023-03-27T21:10:10.860075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0600-0659 41971
 
7.2%
0800-0859 39031
 
6.7%
0700-0759 38450
 
6.6%
1700-1759 37254
 
6.4%
1200-1259 36918
 
6.3%
1100-1159 36774
 
6.3%
1500-1559 35988
 
6.2%
1400-1459 35925
 
6.2%
1000-1059 35768
 
6.1%
1600-1659 35250
 
6.0%
Other values (9) 210656
36.1%

Most occurring characters

ValueCountFrequency (%)
0 1631046
31.0%
1 832679
15.8%
9 716399
13.6%
5 671838
12.8%
- 583985
 
11.1%
2 220852
 
4.2%
6 154442
 
2.9%
7 151408
 
2.9%
8 147402
 
2.8%
3 73964
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4671880
88.9%
Dash Punctuation 583985
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1631046
34.9%
1 832679
17.8%
9 716399
15.3%
5 671838
14.4%
2 220852
 
4.7%
6 154442
 
3.3%
7 151408
 
3.2%
8 147402
 
3.2%
3 73964
 
1.6%
4 71850
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 583985
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5255865
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1631046
31.0%
1 832679
15.8%
9 716399
13.6%
5 671838
12.8%
- 583985
 
11.1%
2 220852
 
4.2%
6 154442
 
2.9%
7 151408
 
2.9%
8 147402
 
2.8%
3 73964
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5255865
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1631046
31.0%
1 832679
15.8%
9 716399
13.6%
5 671838
12.8%
- 583985
 
11.1%
2 220852
 
4.2%
6 154442
 
2.9%
7 151408
 
2.9%
8 147402
 
2.8%
3 73964
 
1.4%

ARR_TIME
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1440
Distinct (%)0.3%
Missing17061
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean1484.3159
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-03-27T21:10:10.948095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile707
Q11104
median1517
Q31919
95-th percentile2251
Maximum2400
Range2399
Interquartile range (IQR)815

Descriptive statistics

Standard deviation523.16285
Coefficient of variation (CV)0.35246058
Kurtosis-0.37072307
Mean1484.3159
Median Absolute Deviation (MAD)408
Skewness-0.35448191
Sum8.4149432 × 108
Variance273699.37
MonotonicityNot monotonic
2023-03-27T21:10:11.047117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1645 674
 
0.1%
2015 664
 
0.1%
1015 661
 
0.1%
1910 657
 
0.1%
1008 656
 
0.1%
2056 654
 
0.1%
1400 651
 
0.1%
2042 650
 
0.1%
1633 650
 
0.1%
1838 650
 
0.1%
Other values (1430) 560357
96.0%
(Missing) 17061
 
2.9%
ValueCountFrequency (%)
1 301
0.1%
2 246
< 0.1%
3 240
< 0.1%
4 237
< 0.1%
5 249
< 0.1%
6 243
< 0.1%
7 222
< 0.1%
8 212
< 0.1%
9 234
< 0.1%
10 215
< 0.1%
ValueCountFrequency (%)
2400 252
< 0.1%
2359 278
< 0.1%
2358 320
0.1%
2357 304
0.1%
2356 301
0.1%
2355 263
< 0.1%
2354 327
0.1%
2353 344
0.1%
2352 362
0.1%
2351 332
0.1%

ARR_DEL15
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing18022
Missing (%)3.1%
Memory size4.5 MiB
0.0
460741 
1.0
105222 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1697889
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 460741
78.9%
1.0 105222
 
18.0%
(Missing) 18022
 
3.1%

Length

2023-03-27T21:10:11.131135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-27T21:10:11.206152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 460741
81.4%
1.0 105222
 
18.6%

Most occurring characters

ValueCountFrequency (%)
0 1026704
60.5%
. 565963
33.3%
1 105222
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1131926
66.7%
Other Punctuation 565963
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1026704
90.7%
1 105222
 
9.3%
Other Punctuation
ValueCountFrequency (%)
. 565963
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1697889
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1026704
60.5%
. 565963
33.3%
1 105222
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1697889
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1026704
60.5%
. 565963
33.3%
1 105222
 
6.2%

CANCELLED
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0.0
567259 
1.0
 
16726

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1751955
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 567259
97.1%
1.0 16726
 
2.9%

Length

2023-03-27T21:10:11.270167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-27T21:10:11.346184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 567259
97.1%
1.0 16726
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 1151244
65.7%
. 583985
33.3%
1 16726
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1167970
66.7%
Other Punctuation 583985
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1151244
98.6%
1 16726
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 583985
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1751955
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1151244
65.7%
. 583985
33.3%
1 16726
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1751955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1151244
65.7%
. 583985
33.3%
1 16726
 
1.0%

DIVERTED
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0.0
582689 
1.0
 
1296

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1751955
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 582689
99.8%
1.0 1296
 
0.2%

Length

2023-03-27T21:10:11.409198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-27T21:10:11.488215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 582689
99.8%
1.0 1296
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1166674
66.6%
. 583985
33.3%
1 1296
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1167970
66.7%
Other Punctuation 583985
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1166674
99.9%
1 1296
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 583985
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1751955
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1166674
66.6%
. 583985
33.3%
1 1296
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1751955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1166674
66.6%
. 583985
33.3%
1 1296
 
0.1%

DISTANCE
Real number (ℝ)

Distinct1451
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean797.74277
Minimum31
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2023-03-27T21:10:11.562232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile168
Q1363
median640
Q31037
95-th percentile2139
Maximum4983
Range4952
Interquartile range (IQR)674

Descriptive statistics

Standard deviation589.99926
Coefficient of variation (CV)0.73958585
Kurtosis2.6283324
Mean797.74277
Median Absolute Deviation (MAD)317
Skewness1.4890764
Sum4.6586981 × 108
Variance348099.13
MonotonicityNot monotonic
2023-03-27T21:10:11.663255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 4167
 
0.7%
296 3277
 
0.6%
733 2875
 
0.5%
214 2463
 
0.4%
594 2411
 
0.4%
404 2383
 
0.4%
236 2330
 
0.4%
447 2297
 
0.4%
328 2211
 
0.4%
335 2192
 
0.4%
Other values (1441) 557379
95.4%
ValueCountFrequency (%)
31 62
 
< 0.1%
45 94
 
< 0.1%
66 62
 
< 0.1%
67 351
0.1%
68 124
 
< 0.1%
69 138
 
< 0.1%
73 390
0.1%
74 383
0.1%
75 532
0.1%
76 204
 
< 0.1%
ValueCountFrequency (%)
4983 66
< 0.1%
4962 62
< 0.1%
4817 18
 
< 0.1%
4502 62
< 0.1%
4243 72
< 0.1%
4184 40
 
< 0.1%
3972 46
 
< 0.1%
3904 62
< 0.1%
3801 62
< 0.1%
3784 124
< 0.1%

Unnamed: 21
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing583985
Missing (%)100.0%
Memory size4.5 MiB

Interactions

2023-03-27T21:10:02.691462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:38.037147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:40.430820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:42.825386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:45.201954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:47.646502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:50.331591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:52.777146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:55.244749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:57.753328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:00.235884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:02.914512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:38.253333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:40.634866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:43.037434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:45.431006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:47.866551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:50.552640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:52.998196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:55.470799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:57.986381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:00.456934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:03.134561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:38.470382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:40.853915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:43.250481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:45.657057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:48.086601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:50.778691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:53.219245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:55.695850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:58.210431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:00.687986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:03.353610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:38.685429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:41.073965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:43.460528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:45.878106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:48.309651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:50.999741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:53.443295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:55.917900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:58.441483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:00.910036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:03.578661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:38.906479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:41.305017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:43.670575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:46.102156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:48.532911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:51.224791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:53.666345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:56.151952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:58.669534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:01.135086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:03.802711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:39.124528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:41.531067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:43.891625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:46.328207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:48.755235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:51.445840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:53.897429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:56.383004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:58.897585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:01.357136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:04.034763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:39.344577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:41.749116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:44.121676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:46.553257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:48.980286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:51.665890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:54.121497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:56.614055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:59.120635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:01.589188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:04.259813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:39.565626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:41.975168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:44.333724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:46.772306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:49.438391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:51.893941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:54.351549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:56.833122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:59.350686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:01.821241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:04.494866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:39.783675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:42.206219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:44.565776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:46.995356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:49.665442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:52.120992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:54.584601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:57.068175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:59.572736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:02.045290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:04.719916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:39.996723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:42.413265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:44.781860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:47.212405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:49.890492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:52.342048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:54.806650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:57.288224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:59.791785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:02.255364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:04.929963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:40.227775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:42.616311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:44.987906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:47.424452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:50.112542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:52.560097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:55.025700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:09:57.516275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:00.010834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-27T21:10:02.473413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-27T21:10:11.768512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
DAY_OF_MONTHDAY_OF_WEEKOP_CARRIER_AIRLINE_IDOP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGIN_AIRPORT_SEQ_IDDEST_AIRPORT_IDDEST_AIRPORT_SEQ_IDDEP_TIMEARR_TIMEDISTANCEOP_UNIQUE_CARRIEROP_CARRIERDEP_DEL15DEP_TIME_BLKARR_DEL15CANCELLEDDIVERTED
DAY_OF_MONTH1.000-0.030-0.004-0.010-0.005-0.005-0.005-0.005-0.0020.003-0.0200.0160.0160.1040.0090.1090.1440.014
DAY_OF_WEEK-0.0301.0000.0110.0600.0060.0060.0060.0060.0030.0050.0200.0250.0250.0390.0190.0390.0440.006
OP_CARRIER_AIRLINE_ID-0.0040.0111.0000.339-0.032-0.032-0.032-0.0320.008-0.017-0.0981.0001.0000.0630.0580.0840.0750.025
OP_CARRIER_FL_NUM-0.0100.0600.3391.000-0.053-0.053-0.059-0.0590.009-0.002-0.3340.4690.4690.0370.0400.0550.0640.012
ORIGIN_AIRPORT_ID-0.0050.006-0.032-0.0531.0001.0000.0190.019-0.033-0.0020.0920.1840.1840.0360.0560.0420.0520.004
ORIGIN_AIRPORT_SEQ_ID-0.0050.006-0.032-0.0531.0001.0000.0190.019-0.033-0.0020.0920.1840.1840.0360.0560.0420.0520.004
DEST_AIRPORT_ID-0.0050.006-0.032-0.0590.0190.0191.0001.0000.0260.0220.0920.1840.1840.0410.0430.0470.0540.015
DEST_AIRPORT_SEQ_ID-0.0050.006-0.032-0.0590.0190.0191.0001.0000.0260.0220.0920.1840.1840.0410.0430.0470.0540.015
DEP_TIME-0.0020.0030.0080.009-0.033-0.0330.0260.0261.0000.781-0.0390.0510.0510.2140.7690.1620.0090.009
ARR_TIME0.0030.005-0.017-0.002-0.002-0.0020.0220.0220.7811.0000.0550.0540.0540.2430.5580.2181.0000.023
DISTANCE-0.0200.020-0.098-0.3340.0920.0920.0920.092-0.0390.0551.0000.1820.1820.0230.0760.0190.0490.017
OP_UNIQUE_CARRIER0.0160.0251.0000.4690.1840.1840.1840.1840.0510.0540.1821.0001.0000.0900.0520.1090.1070.028
OP_CARRIER0.0160.0251.0000.4690.1840.1840.1840.1840.0510.0540.1821.0001.0000.0900.0520.1090.1070.028
DEP_DEL150.1040.0390.0630.0370.0360.0360.0410.0410.2140.2430.0230.0900.0901.0000.1460.7190.0220.021
DEP_TIME_BLK0.0090.0190.0580.0400.0560.0560.0430.0430.7690.5580.0760.0520.0520.1461.0000.1020.0220.008
ARR_DEL150.1090.0390.0840.0550.0420.0420.0470.0470.1620.2180.0190.1090.1090.7190.1021.0001.0001.000
CANCELLED0.1440.0440.0750.0640.0520.0520.0540.0540.0091.0000.0490.1070.1070.0220.0221.0001.0000.008
DIVERTED0.0140.0060.0250.0120.0040.0040.0150.0150.0090.0230.0170.0280.0280.0210.0081.0000.0081.000

Missing values

2023-03-27T21:10:05.316050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-27T21:10:06.307352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-27T21:10:07.806353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DAY_OF_MONTHDAY_OF_WEEKOP_UNIQUE_CARRIEROP_CARRIER_AIRLINE_IDOP_CARRIERTAIL_NUMOP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGIN_AIRPORT_SEQ_IDORIGINDEST_AIRPORT_IDDEST_AIRPORT_SEQ_IDDESTDEP_TIMEDEP_DEL15DEP_TIME_BLKARR_TIMEARR_DEL15CANCELLEDDIVERTEDDISTANCEUnnamed: 21
0129E203639EN8688C3280119531195302GNV103971039707ATL601.00.00600-0659722.00.00.00.0300.0NaN
1129E203639EN348PQ3281134871348702MSP111931119302CVG1359.00.01400-14591633.00.00.00.0596.0NaN
2129E203639EN8896A3282114331143302DTW111931119302CVG1215.00.01200-12591329.00.00.00.0229.0NaN
3129E203639EN8886A3283152491524906TLH103971039707ATL1521.00.01500-15591625.00.00.00.0223.0NaN
4129E203639EN8974C3284103971039707ATL117781177801FSM1847.00.01900-19591940.00.00.00.0579.0NaN
5129E203639EN927EV3285112671126702DAY134871348702MSP853.00.00900-0959953.00.00.00.0574.0NaN
6129E203639EN915XJ3286124481244807JAN103971039707ATL1553.00.01500-15591832.00.00.00.0341.0NaN
7129E203639EN295PQ3287129531295304LGA111931119302CVG1551.00.01500-15591824.00.00.00.0585.0NaN
8129E203639EN337PQ3288124511245102JAX129531295304LGA1037.00.01000-10591239.00.00.00.0833.0NaN
9129E203639EN311PQ3289103971039707ATL106851068502BMI1245.00.01200-12591318.00.00.00.0533.0NaN
DAY_OF_MONTHDAY_OF_WEEKOP_UNIQUE_CARRIEROP_CARRIER_AIRLINE_IDOP_CARRIERTAIL_NUMOP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGIN_AIRPORT_SEQ_IDORIGINDEST_AIRPORT_IDDEST_AIRPORT_SEQ_IDDESTDEP_TIMEDEP_DEL15DEP_TIME_BLKARR_TIMEARR_DEL15CANCELLEDDIVERTEDDISTANCEUnnamed: 21
583975314UA19977UAN813UA205147711477104SFO140571405702PDX604.00.00600-0659802.00.00.00.0550.0NaN
583976314UA19977UAN75861204139301393007ORD128921289208LAX813.01.00700-07591028.00.00.00.01744.0NaN
583977314UA19977UAN39728203106931069302BNA122661226603IAH1105.00.01100-11591335.01.00.00.0657.0NaN
583978314UA19977UAN779UA202139301393007ORD138301383002OGG1027.01.01000-10591457.00.00.00.04184.0NaN
583979314UA19977UAN769UA201121731217305HNL120161201602GUM1502.00.01500-15591849.00.00.00.03801.0NaN
583980314UA19977UAN776UA200120161201602GUM121731217305HNL749.00.00700-07591832.00.00.00.03801.0NaN
583981314UA19977UAN36280174120161201602GUM149551495503SPN717.00.00700-0759759.00.00.00.0129.0NaN
583982314UA19977UAN36280117149551495503SPN120161201602GUM857.00.00900-0959933.00.00.00.0129.0NaN
583983314UA19977UAN39726105149551495503SPN120161201602GUM1820.00.01800-18591854.00.00.00.0129.0NaN
583984314UA19977UAN39726104120161201602GUM149551495503SPN1636.00.01600-16591719.00.00.00.0129.0NaN